Computer Vision: Current Challenges, Trends, and Tools in 2024

Key Challenges Addressed by Computer Vision

1. Autonomous Vehicles. Computer vision plays a crucial role in navigation and decision-making for autonomous cars, using cameras, radars, and LiDAR to recognize objects and ensure safety.
2. Medical Diagnosis. In healthcare, computer vision technologies are actively used for analyzing medical images, improving diagnosis and monitoring of patient conditions.
3. Detection and Prevention of Fakes. Computer vision helps identify fake images and videos, protecting against misinformation and ensuring the integrity of digital content.
4. Augmented and Mixed Reality. AR and MR technologies, supported by computer vision, enable the creation of interactive digital 3D environments used in education, industry, and other fields.
5. Object Detection and Recognition. Improving the accuracy and speed of real-time object detection, especially in challenging conditions (e.g., poor lighting or crowded scenes).
6. Video Analysis. Processing and analyzing video streams to detect anomalies, recognize actions, and track objects.
7. Interpretation and Explanation. Developing methods to explain how models make decisions, which is particularly important in critical applications such as medicine and security.

Current Tools and Technologies

1. OpenCV. This popular library provides numerous functions for image and video processing and supports various programming languages.
2. TensorFlow and PyTorch. Leaders in creating and training neural networks used in modern computer vision systems.
3. Edge Computing. In 2024, the use of edge computing is growing for processing data at the network’s edge, reducing latency and enhancing security. This involves shifting computations to devices (e.g., cameras and mobile phones) for real-time data processing without the need to send it to a server.
4. 3D Computer Vision. 3D visualization technologies are gaining popularity, providing more accurate interactions with objects in three-dimensional space.
5. Detectron2. A platform from Facebook AI Research for object detection and segmentation tasks.
6. YOLO and SSD. Algorithms like YOLO (You Only Look Once) and SSD (Single Shot MultiBox Detector) remain popular for real-time object detection tasks due to their high speed and accuracy.
7. MediaPipe. A library from Google for creating multimodal applications, including gesture and face recognition.
8. DALL-E and Other Generative Models. Generative models like DALL-E open new horizons in image creation and visual content, which is also related to computer vision.

Prospects and Trends for 2024

In 2024, computer vision continues to evolve actively, and several key trends can be highlighted.

The first trend is the use of synthetic data. Synthetic data generated by generative artificial intelligence ( Generative AI) is becoming increasingly in demand. It allows for more efficient training of computer vision models at lower costs.

The second important trend is the need for ethical standards and privacy. As computer vision technologies are implemented in various areas of life, including healthcare, security, and autonomous vehicles, there is a growing need for strict adherence to ethical standards.

Another trend is Deep Learning. The continued use and improvement of deep neural network architectures, such as CNNs and transformers, for computer vision tasks.

In 2024, computer vision not only solidifies its position but also expands the boundaries of what is possible, finding applications across a wide range of industries.